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<table width="100%" summary="page for salinity"><tr><td>salinity</td><td align="right">R Documentation</td></tr></table>

<h2>Salinity Data</h2>

<h3>Description</h3>


<p>This is a data set consisting of measurements of water salinity (i.e.,
its salt concentration) and river discharge taken in North Carolina's
Pamlico Sound; This dataset was listed by Ruppert and Carroll
(1980).  In Carrol and Ruppert (1985) the physical background of the
data is described.  They indicated that observations 5 and 16
correspond to periods of very heavy discharge and showed that the
discrepant observation 5 was masked by observations 3 and 16, i.e.,
only after deletion of these observations it was possible to identify
the influential observation 5.
</p>
<p>This data set is a prime example of the masking effect.
</p>


<h3>Usage</h3>

<pre>data(salinity)</pre>


<h3>Format</h3>


<p>A data frame with 28 observations on the following 4 variables.
</p>

<dl>
<dt><code>X1</code></dt><dd><p>Lagged Salinity</p>
</dd>
<dt><code>X2</code></dt><dd><p>Trend</p>
</dd>
<dt><code>X3</code></dt><dd><p>Discharge</p>
</dd>
<dt><code>Y</code></dt><dd><p>Salinity</p>
</dd>
</dl>



<h3>Source</h3>


<p>P. J. Rousseeuw and A. M. Leroy (1987)
<EM>Robust Regression and Outlier Detection</EM>;
Wiley, p.82, table 5.
</p>


<h3>Examples</h3>

<pre>
data(salinity)
summary(lm.sali  &lt;-        lm(Y ~ . , data = salinity))
summary(rlm.sali &lt;- MASS::rlm(Y ~ . , data = salinity))
summary(lts.sali &lt;-    ltsReg(Y ~ . , data = salinity))

salinity.x &lt;- data.matrix(salinity[, 1:3])
c_sal &lt;- covMcd(salinity.x)
plot(c_sal, "tolEllipsePlot")
</pre>


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